Google Location History as an Alternative Data Source for Understanding Travel Behavior in Medan, Binjai, and Deli Serdang (Mebidang), Indonesia

被引:0
作者
Wismadi, Arif [1 ,2 ]
Narotama, Mohamad Rachmadian [1 ]
Haq, Gary [3 ]
Cinderby, Steve [3 ]
Nugroho, Deni Prasetio [1 ]
Harmanto, Jan Prabowo [1 ]
机构
[1] Univ Gadjah Mada, Ctr Transportat & Logist Studies, Sleman 55281, Indonesia
[2] Univ Islam Indonesia, Fac Civil Engn & Planning, Sleman 55584, Indonesia
[3] Univ York, Stockholm Environm Inst, Environm Dept, York YO10 5DD, England
来源
FUTURE TRANSPORTATION | 2025年 / 5卷 / 02期
关键词
participatory mapping; Google Location History (GLH); mobile phone travel survey; travel behavior; Indonesia; EMISSIONS;
D O I
10.3390/futuretransp5020050
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The performance of urban transport is a critical aspect of a city's functionality, which needs to be supported by innovative data sources to analyze travel patterns. This study explores the use of Google Location History (GLH) as a participatory geographic information system for mobility surveys, offering a cost-effective and more detailed alternative to traditional approaches. GLH is a novel data source with high potential, but still underutilized and underresearched, especially in developing countries. This study uses a new approach in GLH data collection and data processing. Data were collected from 420 respondents in Medan, Binjai, and Deli Serdang (Mebidang) in Indonesia, to examine urban travel patterns, including trip distances, modes, and purposes, while addressing issues of data accuracy, privacy, and representation. GLH provides granular insights into mobility, reducing biases associated with self-reported surveys and identifying discrepancies between stated and actual transport usage. The findings highlight GLH's potential for understanding spatial mobility patterns linked to demographic characteristics and travel purpose in more detail. However, technical challenges, such as data anomalies and the reliance on two devices for data collection, underscore the need to improve location readings and develop add-on tools capable of direct data export for large-scale mobility surveys. This study advances the application of GLH in mobility research, demonstrating its potential use and challenges for large-scale mobility surveys. Future research should address privacy concerns and optimize data collection to enable more inclusive and sustainable urban mobility strategies.
引用
收藏
页数:19
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